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TencentDB for PostgreSQL pgvector HNSW Performance Test Report

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마지막 업데이트 시간: 2026-08-27 16:39:32
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Testing Environment

Configuration Item
Description
Database
RDS PostgreSQL 17
pgvector
0.8.0
Instance Specifications
16 vCPU / 64 GB
Client
4 vCPU / 8 GB (Same VPC)
Test Tool
Stress Test Mode
Single-threaded
Recall Metric
Recall@100

Test data set

Dataset
Level
Vector Count
Distance Type
Typical Scenario
dbpedia-openai-1M
1536
1,000,000
Cosine
OpenAI / Large Model Embedding
NYTimes-256
256
290,000
Cosine
Text similarity search

Testing Method

Preparing the Environment

1. Install ann-benchmarks.
git clone https://github.com/erikbern/ann-benchmarks.git
cd ann-benchmarks
pip install -r requirements.txt
2. Configure the database connection.
Edit ann_benchmarks/algorithms/pgvector/module.py and add the connection information:
import os
os.environ['ANN_BENCHMARKS_PG_USER'] = '<username>'
os.environ['ANN_BENCHMARKS_PG_PASSWORD'] = '<password>'
os.environ['ANN_BENCHMARKS_PG_DBNAME'] = '<database name>'
os.environ['ANN_BENCHMARKS_PG_HOST'] = '<instance private network address>'
os.environ['ANN_BENCHMARKS_PG_PORT'] = '5432'
os.environ['ANN_BENCHMARKS_PG_START_SERVICE'] = 'false'
3. Configure test parameters
Edit ann_benchmarks/algorithms/pgvector/config.yml:
float:
  any:
  - base_args: ['@metric']
    constructor: PGVector
    disabled: false
    docker_tag: ann-benchmarks-pgvector
    module: ann_benchmarks.algorithms.pgvector
    name: pgvector
    run_groups:
      M-16(200):
        arg_groups: [{M: 16, efConstruction: 200}]
        args: {}
        query_args: [[10, 20, 40, 80, 120, 200, 400, 800]]
      M-24(200):
        arg_groups: [{M: 24, efConstruction: 200}]
        args: {}
        query_args: [[10, 20, 40, 80, 120, 200, 400, 800]]
      M-48(256):
        arg_groups: [{M: 48, efConstruction: 256}]
        args: {}
        query_args: [[10, 20, 40, 80, 120, 200, 400, 800]]
4. Build the test Docker image.
python install.py --algorithm pgvector

Executing a Test

# Testing with the dbpedia-openai-1M dataset
python run.py --dataset dbpedia-openai-1000k-angular -k 100 --algorithm pgvector --runs 1

# Testing with the NYTimes-256 dataset
python run.py --dataset nytimes-256-angular -k 100 --algorithm pgvector --runs 1

Result obtaining

# Generate visualization charts
python plot.py --dataset dbpedia-openai-1000k-angular --recompute

# Export detailed CSV data
python data_export.py --out results.csv

Performance Test Results

dbpedia-openai-1M (1536 Dimensions / 1 Million Vectors)

Scenario
Index Configurations
ef_search
Recall@100
QPS
P99(ms)
High recall
m=24, ef_construction=200
120
99.2%
225
7.2
Ultra-high recall
m=48, ef_construction=256
120
99.8%
146
12.3

NYTimes-256 (256 Dimensions / 290,000 Vectors)

Scenario
Index Configurations
ef_search
Recall@100
QPS
P99(ms)
High recall
m=24, ef_construction=200
400
89.0%
101
12.7
Ultra-high recall
m=24, ef_construction=200
800
93.9%
52
23.6
이전 주제: Apache AGE Graph Database Usage Guide다음 주제: Agent Long-Term Memory Feature Overview

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